Boosting Cooperative NPC Effectiveness and Player Immersion through Behavior Tree Optimization in Gaming
Fangyu Zhu · Applied and Computational Engineering · 2025
The purpose of this research is to improve the adaptability and intelligence of cooperative non-player characters (NPCs) in dynamic gaming situations. Although behavior trees (BTs) are commonly used to simulate non-player character (NPC) behaviors, their rigid hierarchical design restricts NPC adaptability in complex settings. This study employs a variety of optimization techniques, including evolutionary algorithms and hybrid strategies that combine Artificial Neural Networks (ANN) and Monte Carlo Tree Search (MCTS), to improve NPC adaptability. The findings reveal that these strategies enable flexible behavior transitions, with evolutionary algorithms strengthening BT's flexibility in decision-making scenarios and ANN and MCTS significantly increasing NPC intelligence and response capabilities. Furthermore, we highlight fundamental drawbacks in typical BTs, such as static node topologies, which may impede dynamic adaptation. These discoveries lay the groundwork for improving immersive gaming experiences, expanding the knowledge repository for NPC AI research, and providing vital insights into the development of more intelligent cooperative NPCs.